{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capital Bikeshare 共享单车租用数量探索分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、数据读取与划分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"day.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据一共有16个属性，15个数值型，日期为对象，没有空数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train=data[data.yr==0]\n",
    "data_test=data[data.yr==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按照要求将数据集分为训练集train和测试集test，划分完后yr,dteday属性没有价值，可以去除；casual,registered按照要求去除。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "认为模型具有时间上按比例增加的性质，引入按天增长率rate，并替换掉intant特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.py:3110: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "rate=pow((data_train['cnt'][364]+data_train['cnt'][363]+data_train['cnt'][362])*1.0/(data_train['cnt'][0]+data_train['cnt'][1]+data_train['cnt'][2]),1.0/362)\n",
    "data_train.instant=(rate)**data_train.instant\n",
    "data_test.instant=(rate)**data_test.instant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train = data_train[['instant','season', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed','cnt']]\n",
    "data_test = data_test[['instant','season', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed','cnt']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、数据探索与特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1、数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols=data_train.columns #cols 训练集属性列表\n",
    "data_train_corr=data_train.corr().abs() #互相关矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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49aSu6Vhs3LCEmrWeIzw8gkaNO6Xv/0LkYVat3gDAqVNh+Pm9wc8/TcmT/DnV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oMr2XJkNZ980yPJcpfq77N6x0LOBh+kiVtDs2Wzl/3KkUve/LH4l7QK9z8ZOeEX6rbsjEe3z9M7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\nGufGGGOMMcbECWucG2OMMcYYEyf+H1AuO3nnNSvlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe832668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_train_corr,annot=True)#互相关热图，图中填充数值\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_train_corr, mask=data_train_corr < 1, cbar=False)\n",
    "plt.savefig('house_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将除了cnt以外，互相关系数大于0.5的属性挑出来,在对离群点处理完毕后去掉冗余的属性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp and atemp = 1.00\n",
      "instant and mnth = 0.99\n",
      "season and mnth = 0.83\n",
      "instant and season = 0.79\n",
      "atemp and cnt = 0.78\n",
      "temp and cnt = 0.77\n",
      "weathersit and hum = 0.58\n",
      "season and cnt = 0.54\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data_train.shape[1]\n",
    "size = data_train_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_train_corr.iloc[i,j] >= threshold and data_train_corr.iloc[i,j] < 1) or (data_train_corr.iloc[i,j] < 0 and data_train_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_train_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FM/C5XbSe8vPC3uM03jgOhzFUuBx0BsNUeVz88Q3jeO1IR1rFBZ976J4rulBashMRSVHh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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf5bccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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q+Umz55jZ8yX9A0l/EF1LBi+WdKWZfdXMHjazW6ILWsfvS/obkv5S0iOSjrp7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\naWwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf6764a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xfc5ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G/P3A/U0VJUlafjzsJkkqwUCSJJVgIEmSSjCQJEklGEiSpBIMJElSCQaSJKkEA0mSVIKB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfdeb828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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blrUznFqpSspw1bqEV9gwaBHZZDcYeTVHkxhP4+3hhOluvFZtqPR9mAWj/EaQ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\nIiIKDQYtIiIKDQYtIiIKDQYtIiIKjf8fCZa+I8URiQEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xffa1390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mHSr2DSQtj8/uOcccN5Upa3SsFC271TfPyVyZkRJv+YY+DI1kQtlNoh6g0SLE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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10267780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ApJglpHyhrL9d2q5Fb5pe10PKF8pqZadOAPAqVgkpnTAtPKdNtz6+vW4dUppr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\nBACIBBISACAS/j8dJT5vVBevGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fe7f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/Jg9H3Vun/kYR6k2x0PG5zclSoePmVJzFckOuWOW9fE6LVd6JzgCrhUcXCx1H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\nRUREkcGgRUREkfH/A68VUxiE301rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11352a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of only the highly correlated pairs\n",
    "for v,i,j in s_corr_list:\n",
    "    sns.pairplot(data_train, size=6, x_vars=cols[i],y_vars=cols[j] )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作相关性较强变量两两间的散点图分布，删除其中的离群点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train = data_train[(data_train.atemp< 0.6) | (data_train.cnt> 1500)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train = data_train[(data_train.season==1) | (data_train.cnt>1200)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train = data_train[(data_train.weathersit< 3) | (data_train.hum> 0.2)] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作单个变量分布并处理离群点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U7FbgiHOuGPgRcJ+3bAmwGJgCLAQeNLP4bvp8ApgEnAGkAl/o0RqKRCjnHP/8hzKyBifx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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114825c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data_train.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('cnt', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Db6L7MIOqej/dVben0A34+4FX9M/dkeStdOvtALylHvyr2qTreiPdHNt7u89U2FHdgkUH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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114790f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data_train.weathersit.values, bins=30, kde=False)\n",
    "plt.xlabel('weathersit', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0sz6gE7jBaRaE77R09fLmznpmjx2RkOueJ5KFE4vITE1mydZaPjKl1Oty5BQNGO7OuRsH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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11767a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data_train.atemp.values, bins=30, kde=True)\n",
    "plt.xlabel('atemp', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+p5ZL52iGpCS+nIw0vnjnEn507xWEwo4PPbCBTz34Ko2dWqQqWalwj/GLLUf5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116174e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data_train.windspeed.values, bins=40, kde=True)\n",
    "plt.xlabel('windspeed', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train = data_train[data_train.windspeed < 0.4] #认为大于0.4为离群点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1173e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data_train.season);\n",
    "plt.xlabel('season');\n",
    "plt.ylabel('Number of season');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8B0KS5r0up5h+p291D2CCaZ72JkmaP7pcxdT/XIin6D3HYdVQqpEkjY0uYxC79VwISdJz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1197c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data_train.holiday);\n",
    "plt.xlabel('holiday');\n",
    "plt.ylabel('Number of holiday');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bsKqWVdWSqlqyYMGCsQWUpKEZdxFcCZzUvD8JuGLM3y9JeoYuLx+9GLgZ2C/JuiSnAB8B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\nNHAWgSQNnEUgSQNnEUjSwFkEkjRw/w8LFILau/W2cwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe8ef208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data_train.weekday);\n",
    "plt.xlabel('weekday');\n",
    "plt.ylabel('Number of weekday');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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idJnF9CRV9Rm8B0KS5rwuXUyvG9vdDFjME11OkqQ5qssspvHnQqwFVgKH9lKNJGlidBmD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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1146ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data_train.workingday);\n",
    "plt.xlabel('workingday');\n",
    "plt.ylabel('Number of workingday');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train = data_train.drop('temp', axis = 1)\n",
    "data_train = data_train.drop('mnth', axis = 1)\n",
    "data_train = data_train.drop('hum', axis = 1)\n",
    "data_test = data_test.drop('temp', axis = 1)\n",
    "data_test = data_test.drop('mnth', axis = 1)\n",
    "data_test = data_test.drop('hum', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将两个属性中与cnt相关系数小的属性去掉（冗余）,考虑到用户随着时间增加而增加，season和mnth均不能表达这种逻辑，因此保留instant属性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2、特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从训练集和测试集数据中分离输入特征X和输出Y \n",
    "Y_train = data_train['cnt'].values\n",
    "X_train = data_train.drop('cnt', axis = 1)\n",
    "Y_test = data_test['cnt'].values\n",
    "X_test = data_test.drop('cnt', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# 数据标准化 X的数值属性进行标准化，类别属性用独热编码增加特征数，可以有效度量每个状态带来的影响，Y值进行归一化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "ss_X = StandardScaler()\n",
    "mms= MinMaxScaler()\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train_c = ss_X.fit_transform(X_train[['instant','weathersit','atemp','windspeed']])\n",
    "X_test_c = ss_X.transform(X_test[['instant','weathersit','atemp','windspeed']])\n",
    "\n",
    "\n",
    "Y_train=mms.fit_transform(Y_train.reshape(-1, 1))\n",
    "Y_test=mms.transform(Y_test.reshape(-1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ohe = OneHotEncoder(sparse=False)\n",
    "X_train_d = ohe.fit_transform(X_train[['season','holiday','weekday','workingday']])\n",
    "X_test_d = ohe.transform(X_test[['season','holiday','weekday','workingday']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_finally=np.hstack((X_train_d,X_train_c))\n",
    "X_test_finally=np.hstack((X_test_d,X_test_c))\n",
    "Y_train_finally=Y_train\n",
    "Y_test_finally=Y_test\n",
    "#编码后的属性标签，独热编码未对每个状态区分\n",
    "columns=['season']*4+['holiday']*2+['weekday']*7+['workingday']*2+['instant','weathersit','atemp','windspeed']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4、岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is -0.0561363728619\n",
      "The r2 score of RidgeCV on train is 0.789559297347\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train_finally, Y_train_finally)    \n",
    "\n",
    "#预测\n",
    "Y_test_pred_ridge = ridge.predict(X_test_finally)\n",
    "Y_train_pred_ridge = ridge.predict(X_train_finally)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(Y_test_finally, Y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(Y_train_finally, Y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xfb192b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 10.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[0.136167621366]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.0598560834289]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.0437147790979]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[0.0262315901846]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.0210245411044]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[0.01813006138]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.0158973998827]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[0.0126389365859]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[0.00290648377664]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[-0.00169173068161]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[-0.00290648377664]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-0.00325331342091]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-0.00806941906498]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.0126153895245]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[-0.0132406740785]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-0.0158973998827]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[-0.0172352560243]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[-0.0542269589567]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-0.124595403631]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             coef_ridge     columns\n",
       "17     [0.136167621366]       atemp\n",
       "3     [0.0598560834289]      season\n",
       "1     [0.0437147790979]      season\n",
       "12    [0.0262315901846]     weekday\n",
       "2     [0.0210245411044]      season\n",
       "15      [0.01813006138]     instant\n",
       "4     [0.0158973998827]     holiday\n",
       "11    [0.0126389365859]     weekday\n",
       "14   [0.00290648377664]  workingday\n",
       "8   [-0.00169173068161]     weekday\n",
       "13  [-0.00290648377664]  workingday\n",
       "10  [-0.00325331342091]     weekday\n",
       "7   [-0.00806941906498]     weekday\n",
       "9    [-0.0126153895245]     weekday\n",
       "6    [-0.0132406740785]     weekday\n",
       "5    [-0.0158973998827]     holiday\n",
       "18   [-0.0172352560243]   windspeed\n",
       "16   [-0.0542269589567]  weathersit\n",
       "0     [-0.124595403631]      season"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取不同alpha对应的岭回归下的目标函数值\n",
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "#做出目标函数值关于alpha对数下的曲线\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_ridge'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5、Lasso回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is -0.199609772591\n",
      "The r2 score of LassoCV on train is 0.784249320376\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "#lasso = LassoCV(alphas=alphas)  \n",
    "lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train_finally, Y_train_finally)  \n",
    "\n",
    "#测试\n",
    "Y_test_pred_lasso = lasso.predict(X_test_finally)\n",
    "Y_train_pred_lasso = lasso.predict(X_train_finally)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of LassoCV on test is', r2_score(Y_test_finally, Y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(Y_train_finally, Y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf9e3240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.0033251600149781565)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.127987</td>\n",
       "      <td>[0.136167621366]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015791</td>\n",
       "      <td>[0.0598560834289]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.0437147790979]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.005455</td>\n",
       "      <td>[0.0262315901846]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[0.0210245411044]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.013985</td>\n",
       "      <td>[0.01813006138]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.0158973998827]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.0126389365859]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.00290648377664]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-0.00169173068161]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.00290648377664]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.00325331342091]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.00806941906498]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.0126153895245]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.0132406740785]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.0158973998827]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.015124</td>\n",
       "      <td>[-0.0172352560243]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.052715</td>\n",
       "      <td>[-0.0542269589567]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.179406</td>\n",
       "      <td>[-0.124595403631]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coef_lasso           coef_ridge     columns\n",
       "17    0.127987     [0.136167621366]       atemp\n",
       "3     0.015791    [0.0598560834289]      season\n",
       "1     0.000000    [0.0437147790979]      season\n",
       "12    0.005455    [0.0262315901846]     weekday\n",
       "2    -0.000000    [0.0210245411044]      season\n",
       "15    0.013985      [0.01813006138]     instant\n",
       "4     0.000000    [0.0158973998827]     holiday\n",
       "11    0.000000    [0.0126389365859]     weekday\n",
       "14    0.000000   [0.00290648377664]  workingday\n",
       "8     0.000000  [-0.00169173068161]     weekday\n",
       "13   -0.000000  [-0.00290648377664]  workingday\n",
       "10   -0.000000  [-0.00325331342091]     weekday\n",
       "7    -0.000000  [-0.00806941906498]     weekday\n",
       "9    -0.000000   [-0.0126153895245]     weekday\n",
       "6    -0.000000   [-0.0132406740785]     weekday\n",
       "5    -0.000000   [-0.0158973998827]     holiday\n",
       "18   -0.015124   [-0.0172352560243]   windspeed\n",
       "16   -0.052715   [-0.0542269589567]  weathersit\n",
       "0    -0.179406    [-0.124595403631]      season"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()                \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_ridge'],ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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